【摘要】 为了克服传统预测方法的不足,采用RS-BPNN模型预测商品房价格.利用粗糙集理论确定影响商品房价格的主要因素,运用具有超强数据处理能力的BP神经网络,根据筛选的主要因素作为输入节点数,构建商品房价格的预测模型,然后通过实例进行仿真.结果表明,其预测精度远远超过传统预测方法.可见RS-BPNN模型在房地产价格预测领域具有很强的实用性.更多还原

【Abstract】 In order to solve the shortcomings of the traditional methods,the RS-BPNN model has been used to predict the price of commercial housing.Firstly,determine the main factors of the commercial housing price in rough set theory.Then build BP neural network which own strong data processing ability,the number of input nodes are determine’according to the main selected factors.An example has been used to simulated,and the prediction is accuracy than traditional methods To some extent,the RS-BPNN model in the real estate price forecasting has strong practicability.更多还原